A linear programming model for power system planning with hydrogen integration
水素統合を考慮した電力システム計画のための線形計画モデル (AI 翻訳)
Issa Zaiter, Andrei Sleptchenko, Ahmad Mayyas, Raed Jaradat, T. Mezher
🤖 gxceed AI 要約
日本語
本論文は、水素をエネルギー貯蔵および産業用燃料として統合する電力システムの最適化モデルを開発した。UAEの2030年目標に対して太陽光、風力、原子力、天然ガスを組み合わせ、コスト最小化を実現した結果、水素の経済的有効性が確認された。炭素税やガス価格変動への脆弱性も明らかにされた。
English
This study develops an hourly-resolution linear programming model for power system planning with hydrogen integration as storage and industrial commodity. Applied to UAE's 2030 targets, the cost-optimal system achieves LCOE $0.065/kWh and hydrogen cost $2.56/kg, demonstrating hydrogen's cost-effectiveness while highlighting vulnerability to carbon pricing and gas price volatility.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本でも水素の大規模導入が検討されており、本モデルは日本の電力系統における水素蓄電・産業用水素の最適構成評価に応用可能。特に炭素価格の影響分析は日本のGX政策における炭素賦課金議論に示唆を与える。
In the global GX context
The model provides a rigorous framework for assessing hydrogen's role in power systems globally, particularly relevant for countries with high renewable penetration and carbon pricing mechanisms. It underscores hydrogen's viability as both storage and industrial feedstock, critical for integrated energy planning under net-zero targets.
👥 読者別の含意
🔬研究者:This paper presents a novel linear programming model that can be adapted for other regions to assess hydrogen's optimal role in power systems.
🏢実務担当者:Corporate energy planners can use this model to evaluate cost-optimal configurations for hydrogen integration in their own portfolios.
🏛政策担当者:Policymakers should note the model's findings on carbon tax and gas price sensitivity, informing hydrogen subsidy and carbon pricing design.
📄 Abstract（原文）
The imperative to decarbonize the economy has positioned hydrogen as a clean fuel for hard-to-abate sectors and as a buffer for renewable energy integration. For the United Arab Emirates (UAE), with abundant solar resources, the large-scale role of hydrogen in the power system remains underexplored. This study develops an hourly-resolution linear programming optimization model to evaluate the economic and environmental benefits of hydrogen integration as both an energy storage medium and an industrial commodity. The model optimizes solar PV, wind, nuclear, and natural gas generation, along with batteries and a hydrogen subsystem including underground hydrogen storage, to meet projected 2030 electricity (203 TWh) and hydrogen (1.4 Mton) demand targets. Implemented in Python and solved with Gurobi, the model identifies a cost-optimal system that yields a levelized cost of electricity of $0.065/kWh and hydrogen of $2.56/kg. The results highlight vulnerability to carbon taxation and gas price volatility, while confirming hydrogen’s cost-effectiveness for storage and demand supply.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1038/s41598-026-35701-4first seen 2026-05-15 20:31:07
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